Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations2803
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory284.8 KiB
Average record size in memory104.0 B

Variable types

Numeric12
Categorical1

Alerts

Area is highly overall correlated with Convex_Area and 1 other fieldsHigh correlation
Compactness is highly overall correlated with Extent and 2 other fieldsHigh correlation
Convex_Area is highly overall correlated with Area and 1 other fieldsHigh correlation
Extent is highly overall correlated with Compactness and 1 other fieldsHigh correlation
Length is highly overall correlated with PerimeterHigh correlation
Perimeter is highly overall correlated with Area and 3 other fieldsHigh correlation
Solidity is highly overall correlated with Compactness and 1 other fieldsHigh correlation

Reproduction

Analysis started2024-10-14 18:00:00.125290
Analysis finished2024-10-14 18:00:23.104270
Duration22.98 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

Length
Real number (ℝ)

HIGH CORRELATION 

Distinct1944
Distinct (%)69.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean289.92638
Minimum151.33527
Maximum515.35248
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.0 KiB
2024-10-14T21:00:23.222796image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum151.33527
5-th percentile203.6737
Q1245.02597
median279.87787
Q3329.15091
95-th percentile411.35281
Maximum515.35248
Range364.01721
Interquartile range (IQR)84.124939

Descriptive statistics

Standard deviation62.181171
Coefficient of variation (CV)0.21447228
Kurtosis0.068361152
Mean289.92638
Median Absolute Deviation (MAD)39.904999
Skewness0.63684484
Sum812663.64
Variance3866.498
MonotonicityNot monotonic
2024-10-14T21:00:23.468949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
252.4578857 5
 
0.2%
241.2635498 5
 
0.2%
254.4638672 5
 
0.2%
244.8665924 4
 
0.1%
296.0483093 4
 
0.1%
271.1351929 4
 
0.1%
342.9841003 4
 
0.1%
350.3298645 4
 
0.1%
310.4946899 4
 
0.1%
269.8413696 4
 
0.1%
Other values (1934) 2760
98.5%
ValueCountFrequency (%)
151.3352661 1
 
< 0.1%
154.7952423 2
0.1%
156.0432281 1
 
< 0.1%
156.4661713 1
 
< 0.1%
159.2402954 1
 
< 0.1%
159.8836517 1
 
< 0.1%
160.4595642 1
 
< 0.1%
160.5242767 2
0.1%
167.8253021 3
0.1%
168.6890869 2
0.1%
ValueCountFrequency (%)
515.352478 1
< 0.1%
512.6253052 2
0.1%
506.372406 1
< 0.1%
499.4121399 1
< 0.1%
497.4573059 1
< 0.1%
494.1210327 1
< 0.1%
481.2356873 1
< 0.1%
477.2573853 1
< 0.1%
475.8347168 1
< 0.1%
469.9933167 1
< 0.1%

Width
Real number (ℝ)

Distinct1859
Distinct (%)66.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean170.8074
Minimum88.050529
Maximum258.56979
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.0 KiB
2024-10-14T21:00:23.642767image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum88.050529
5-th percentile126.3836
Q1149.5894
median169.92412
Q3190.64043
95-th percentile224.59663
Maximum258.56979
Range170.51926
Interquartile range (IQR)41.051025

Descriptive statistics

Standard deviation29.587914
Coefficient of variation (CV)0.17322384
Kurtosis-0.050047453
Mean170.8074
Median Absolute Deviation (MAD)20.577881
Skewness0.19561051
Sum478773.15
Variance875.44468
MonotonicityNot monotonic
2024-10-14T21:00:23.838929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
169.46492 5
 
0.2%
184.4487152 5
 
0.2%
171.9005585 4
 
0.1%
134.7543488 4
 
0.1%
206.7833099 4
 
0.1%
204.3783875 4
 
0.1%
175.6868896 4
 
0.1%
132.1011047 4
 
0.1%
213.1753845 4
 
0.1%
162.4747772 4
 
0.1%
Other values (1849) 2761
98.5%
ValueCountFrequency (%)
88.05052948 1
< 0.1%
88.05123138 2
0.1%
90.07479095 1
< 0.1%
90.56969452 1
< 0.1%
92.25747681 1
< 0.1%
92.62679291 2
0.1%
93.73306274 2
0.1%
93.8094101 2
0.1%
94.20102692 1
< 0.1%
94.51618195 2
0.1%
ValueCountFrequency (%)
258.5697937 1
< 0.1%
257.7919006 1
< 0.1%
257.3881836 1
< 0.1%
256.3813782 1
< 0.1%
255.9151306 1
< 0.1%
254.0734253 1
< 0.1%
253.7433777 1
< 0.1%
253.3808289 1
< 0.1%
253.0660858 1
< 0.1%
251.3216553 1
< 0.1%

Thickness
Real number (ℝ)

Distinct1797
Distinct (%)64.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean109.79245
Minimum59.494278
Maximum181.8452
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.0 KiB
2024-10-14T21:00:24.043144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum59.494278
5-th percentile78.197243
Q197.348965
median110.44627
Q3121.59585
95-th percentile140.16505
Maximum181.8452
Range122.35092
Interquartile range (IQR)24.246883

Descriptive statistics

Standard deviation18.94619
Coefficient of variation (CV)0.17256369
Kurtosis0.22639383
Mean109.79245
Median Absolute Deviation (MAD)11.84491
Skewness0.11469466
Sum307748.23
Variance358.9581
MonotonicityNot monotonic
2024-10-14T21:00:24.218877image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
102.5193939 5
 
0.2%
125.8632965 5
 
0.2%
128.172821 4
 
0.1%
102.9962845 4
 
0.1%
86.40414429 4
 
0.1%
131.7304077 4
 
0.1%
123.282135 4
 
0.1%
110.7969055 4
 
0.1%
83.44372559 4
 
0.1%
100.4527206 4
 
0.1%
Other values (1787) 2761
98.5%
ValueCountFrequency (%)
59.49427795 1
 
< 0.1%
59.99125671 2
0.1%
60.47774506 3
0.1%
60.52585983 1
 
< 0.1%
61.58906555 1
 
< 0.1%
62.78192902 2
0.1%
63.36927414 1
 
< 0.1%
63.89343643 2
0.1%
65.5340271 2
0.1%
66.75514221 1
 
< 0.1%
ValueCountFrequency (%)
181.8451996 1
 
< 0.1%
181.4137115 1
 
< 0.1%
181.0529785 1
 
< 0.1%
179.3300171 3
0.1%
171.271286 2
0.1%
170.9720154 1
 
< 0.1%
169.5546112 2
0.1%
168.8439178 2
0.1%
164.6936035 1
 
< 0.1%
162.1138458 1
 
< 0.1%

Area
Real number (ℝ)

HIGH CORRELATION 

Distinct2750
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26511.117
Minimum6037
Maximum89282
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.0 KiB
2024-10-14T21:00:24.762680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum6037
5-th percentile10611.25
Q116211.5
median23440.5
Q333451
95-th percentile54367.3
Maximum89282
Range83245
Interquartile range (IQR)17239.5

Descriptive statistics

Standard deviation13782.561
Coefficient of variation (CV)0.51987855
Kurtosis1.6273588
Mean26511.117
Median Absolute Deviation (MAD)7998.5
Skewness1.2424311
Sum74310662
Variance1.89959 × 108
MonotonicityNot monotonic
2024-10-14T21:00:24.941672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10535 3
 
0.1%
13978 2
 
0.1%
20190.5 2
 
0.1%
14676.5 2
 
0.1%
67056 2
 
0.1%
29979 2
 
0.1%
26640.5 2
 
0.1%
31187 2
 
0.1%
6499.5 2
 
0.1%
14873.5 2
 
0.1%
Other values (2740) 2782
99.3%
ValueCountFrequency (%)
6037 1
< 0.1%
6177 1
< 0.1%
6185 1
< 0.1%
6198.5 1
< 0.1%
6499.5 2
0.1%
6574.5 1
< 0.1%
6755 1
< 0.1%
6819.5 1
< 0.1%
6898 1
< 0.1%
6915.5 1
< 0.1%
ValueCountFrequency (%)
89282 1
< 0.1%
86040 1
< 0.1%
85880 1
< 0.1%
83822 1
< 0.1%
81813 1
< 0.1%
79559 1
< 0.1%
78531.5 1
< 0.1%
78338.5 1
< 0.1%
78242 1
< 0.1%
77816.5 1
< 0.1%

Perimeter
Real number (ℝ)

HIGH CORRELATION 

Distinct2793
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean743.86377
Minimum311.56349
Maximum1864.9474
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.0 KiB
2024-10-14T21:00:25.103625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum311.56349
5-th percentile436.05264
Q1571.73001
median707.48737
Q3878.89653
95-th percentile1151.6952
Maximum1864.9474
Range1553.3839
Interquartile range (IQR)307.16652

Descriptive statistics

Standard deviation230.63208
Coefficient of variation (CV)0.31004612
Kurtosis1.1297973
Mean743.86377
Median Absolute Deviation (MAD)151.65686
Skewness0.91904927
Sum2085050.1
Variance53191.154
MonotonicityNot monotonic
2024-10-14T21:00:25.343673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
954.6244514 2
 
0.1%
547.7300085 2
 
0.1%
881.244728 2
 
0.1%
726.2569201 2
 
0.1%
713.7716388 2
 
0.1%
557.3868634 2
 
0.1%
460.9604578 2
 
0.1%
692.4995612 2
 
0.1%
1437.425527 2
 
0.1%
506.5168068 2
 
0.1%
Other values (2783) 2783
99.3%
ValueCountFrequency (%)
311.5634888 1
< 0.1%
320.7350618 1
< 0.1%
320.9777024 1
< 0.1%
321.0782073 1
< 0.1%
340.7350615 1
< 0.1%
341.3208473 1
< 0.1%
342.0487697 1
< 0.1%
346.9777017 1
< 0.1%
348.29141 1
< 0.1%
348.4924203 1
< 0.1%
ValueCountFrequency (%)
1864.947387 1
< 0.1%
1732.999123 1
< 0.1%
1719.935194 1
< 0.1%
1684.898617 1
< 0.1%
1675.082383 1
< 0.1%
1651.082382 1
< 0.1%
1627.803165 1
< 0.1%
1609.259008 1
< 0.1%
1606.898618 1
< 0.1%
1593.685414 1
< 0.1%

Roundness
Real number (ℝ)

Distinct1944
Distinct (%)69.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.46952578
Minimum0.17374846
Maximum0.69729264
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.0 KiB
2024-10-14T21:00:25.600620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.17374846
5-th percentile0.25480081
Q10.38359415
median0.47143167
Q30.57732421
95-th percentile0.63839578
Maximum0.69729264
Range0.52354419
Interquartile range (IQR)0.19373006

Descriptive statistics

Standard deviation0.11870449
Coefficient of variation (CV)0.25281783
Kurtosis-0.87364686
Mean0.46952578
Median Absolute Deviation (MAD)0.097720723
Skewness-0.30042357
Sum1316.0808
Variance0.014090756
MonotonicityNot monotonic
2024-10-14T21:00:25.813416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6323167105 7
 
0.2%
0.4142495572 5
 
0.2%
0.5977807185 4
 
0.1%
0.4535270637 4
 
0.1%
0.4947791534 4
 
0.1%
0.3682207855 4
 
0.1%
0.4591370937 4
 
0.1%
0.5100017618 4
 
0.1%
0.5789729752 4
 
0.1%
0.4289546782 4
 
0.1%
Other values (1934) 2759
98.4%
ValueCountFrequency (%)
0.1737484552 2
0.1%
0.1783044211 2
0.1%
0.1811091857 1
< 0.1%
0.1850221679 1
< 0.1%
0.1881483404 1
< 0.1%
0.1943163767 2
0.1%
0.194803299 2
0.1%
0.2021449064 2
0.1%
0.2048341426 1
< 0.1%
0.2133799222 1
< 0.1%
ValueCountFrequency (%)
0.6972926432 1
< 0.1%
0.6855770595 1
< 0.1%
0.6803785287 2
0.1%
0.6793269392 1
< 0.1%
0.6773676637 1
< 0.1%
0.6764886335 2
0.1%
0.6741675939 2
0.1%
0.673256447 1
< 0.1%
0.6706803041 2
0.1%
0.6692031068 1
< 0.1%

Solidity
Real number (ℝ)

HIGH CORRELATION 

Distinct2800
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.95582807
Minimum0.71877246
Maximum0.99288912
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.0 KiB
2024-10-14T21:00:26.019004image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.71877246
5-th percentile0.8706016
Q10.94457906
median0.97042221
Q30.98148402
95-th percentile0.98862805
Maximum0.99288912
Range0.27411666
Interquartile range (IQR)0.036904957

Descriptive statistics

Standard deviation0.039595724
Coefficient of variation (CV)0.041425572
Kurtosis5.4602108
Mean0.95582807
Median Absolute Deviation (MAD)0.014092448
Skewness-2.1749702
Sum2679.1861
Variance0.0015678214
MonotonicityNot monotonic
2024-10-14T21:00:26.201666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7187724634 2
 
0.1%
0.9685987384 2
 
0.1%
0.9258937078 2
 
0.1%
0.9153351932 1
 
< 0.1%
0.9265368827 1
 
< 0.1%
0.9431130848 1
 
< 0.1%
0.9430423355 1
 
< 0.1%
0.9460082822 1
 
< 0.1%
0.9364214116 1
 
< 0.1%
0.9400149134 1
 
< 0.1%
Other values (2790) 2790
99.5%
ValueCountFrequency (%)
0.7187724634 2
0.1%
0.7414402427 1
< 0.1%
0.7450065648 1
< 0.1%
0.7511175547 1
< 0.1%
0.7617977528 1
< 0.1%
0.7671826431 1
< 0.1%
0.7686141566 1
< 0.1%
0.7713918269 1
< 0.1%
0.7841960295 1
< 0.1%
0.7853451096 1
< 0.1%
ValueCountFrequency (%)
0.992889123 1
< 0.1%
0.9927057971 1
< 0.1%
0.9924222343 1
< 0.1%
0.9922863139 1
< 0.1%
0.992081448 1
< 0.1%
0.9920251382 1
< 0.1%
0.9919179942 1
< 0.1%
0.9919061562 1
< 0.1%
0.9918980822 1
< 0.1%
0.9918912921 1
< 0.1%

Compactness
Real number (ℝ)

HIGH CORRELATION 

Distinct2800
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8252335
Minimum1.1644687
Maximum9.6600571
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.0 KiB
2024-10-14T21:00:26.392342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.1644687
5-th percentile1.2486428
Q11.3573981
median1.5764123
Q31.9659529
95-th percentile3.3775808
Maximum9.6600571
Range8.4955883
Interquartile range (IQR)0.60855473

Descriptive statistics

Standard deviation0.79405843
Coefficient of variation (CV)0.43504485
Kurtosis14.887234
Mean1.8252335
Median Absolute Deviation (MAD)0.2592854
Skewness3.2181026
Sum5116.1295
Variance0.63052879
MonotonicityNot monotonic
2024-10-14T21:00:26.604960image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.457891663 2
 
0.1%
2.229812351 2
 
0.1%
2.452015431 2
 
0.1%
1.844151746 1
 
< 0.1%
2.284923767 1
 
< 0.1%
1.944841272 1
 
< 0.1%
2.274092729 1
 
< 0.1%
2.207729603 1
 
< 0.1%
2.466474898 1
 
< 0.1%
2.14965433 1
 
< 0.1%
Other values (2790) 2790
99.5%
ValueCountFrequency (%)
1.164468727 1
< 0.1%
1.16746807 1
< 0.1%
1.171493171 1
< 0.1%
1.172427662 1
< 0.1%
1.175407185 1
< 0.1%
1.179803389 1
< 0.1%
1.180105672 1
< 0.1%
1.182508941 1
< 0.1%
1.183430673 1
< 0.1%
1.184406026 1
< 0.1%
ValueCountFrequency (%)
9.660057066 1
< 0.1%
9.085459347 1
< 0.1%
7.518157797 1
< 0.1%
7.052304174 1
< 0.1%
6.825683957 1
< 0.1%
6.732644849 1
< 0.1%
6.704805865 1
< 0.1%
6.596767535 1
< 0.1%
6.488794301 1
< 0.1%
6.457891663 2
0.1%

Aspect_Ratio
Real number (ℝ)

Distinct1003
Distinct (%)35.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7542552
Minimum1.4000817
Maximum2.7312514
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.0 KiB
2024-10-14T21:00:26.804996image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.4000817
5-th percentile1.5183611
Q11.6152884
median1.7059238
Q31.838599
95-th percentile2.1539618
Maximum2.7312514
Range1.3311697
Interquartile range (IQR)0.22331051

Descriptive statistics

Standard deviation0.20393376
Coefficient of variation (CV)0.11625091
Kurtosis2.0536555
Mean1.7542552
Median Absolute Deviation (MAD)0.10548497
Skewness1.302701
Sum4917.1774
Variance0.041588979
MonotonicityNot monotonic
2024-10-14T21:00:27.032479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.548957718 8
 
0.3%
1.600438803 7
 
0.2%
1.864239083 7
 
0.2%
1.800293087 7
 
0.2%
1.577732151 7
 
0.2%
1.767010353 7
 
0.2%
2.111400983 7
 
0.2%
1.689471263 7
 
0.2%
1.880837863 6
 
0.2%
1.641675485 6
 
0.2%
Other values (993) 2734
97.5%
ValueCountFrequency (%)
1.400081673 5
0.2%
1.427405508 6
0.2%
1.436017711 2
 
0.1%
1.449457939 2
 
0.1%
1.45116974 4
0.1%
1.452477741 3
0.1%
1.462166258 2
 
0.1%
1.464647005 2
 
0.1%
1.466764392 2
 
0.1%
1.468290452 4
0.1%
ValueCountFrequency (%)
2.73125138 3
0.1%
2.607367523 1
 
< 0.1%
2.606582831 4
0.1%
2.598485293 2
0.1%
2.580846925 2
0.1%
2.579810661 3
0.1%
2.573946228 1
 
< 0.1%
2.527518166 4
0.1%
2.506223458 2
0.1%
2.468742797 2
0.1%

Eccentricity
Real number (ℝ)

Distinct1003
Distinct (%)35.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.81361199
Minimum0.69989674
Maximum0.93056271
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.0 KiB
2024-10-14T21:00:27.224145image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.69989674
5-th percentile0.75251646
Q10.78460362
median0.81032867
Q30.83966301
95-th percentile0.88790298
Maximum0.93056271
Range0.23066598
Interquartile range (IQR)0.055059392

Descriptive statistics

Standard deviation0.041301826
Coefficient of variation (CV)0.050763542
Kurtosis-0.37560752
Mean0.81361199
Median Absolute Deviation (MAD)0.027451218
Skewness0.29789897
Sum2280.5544
Variance0.0017058409
MonotonicityNot monotonic
2024-10-14T21:00:27.404121image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7577382187 8
 
0.3%
0.8340819394 7
 
0.2%
0.8133212007 7
 
0.2%
0.7648571685 7
 
0.2%
0.8556213928 7
 
0.2%
0.8342153168 7
 
0.2%
0.799227214 7
 
0.2%
0.8664939375 7
 
0.2%
0.809647096 7
 
0.2%
0.8947935603 7
 
0.2%
Other values (993) 2732
97.5%
ValueCountFrequency (%)
0.6998967364 1
 
< 0.1%
0.7135819492 3
0.1%
0.7176827201 3
0.1%
0.7238922111 4
0.1%
0.724666961 2
 
0.1%
0.7252565525 2
 
0.1%
0.7295600553 2
 
0.1%
0.7306442431 4
0.1%
0.731564017 5
0.2%
0.7322237453 3
0.1%
ValueCountFrequency (%)
0.9305627139 3
0.1%
0.9235290021 4
0.1%
0.9234810453 2
0.1%
0.9229834746 1
 
< 0.1%
0.9218824365 4
0.1%
0.9218170052 3
0.1%
0.9214451377 2
0.1%
0.9184035118 2
0.1%
0.9169480017 1
 
< 0.1%
0.9142881235 2
0.1%

Extent
Real number (ℝ)

HIGH CORRELATION 

Distinct2800
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.72458678
Minimum0.45453779
Maximum0.84581251
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.0 KiB
2024-10-14T21:00:27.590330image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.45453779
5-th percentile0.63064192
Q10.70167312
median0.73371998
Q30.75755057
95-th percentile0.78175184
Maximum0.84581251
Range0.39127472
Interquartile range (IQR)0.055877458

Descriptive statistics

Standard deviation0.04747387
Coefficient of variation (CV)0.065518543
Kurtosis2.6796559
Mean0.72458678
Median Absolute Deviation (MAD)0.026578736
Skewness-1.3329531
Sum2031.0168
Variance0.0022537683
MonotonicityNot monotonic
2024-10-14T21:00:27.808433image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5790716322 2
 
0.1%
0.6921828172 2
 
0.1%
0.6859948849 2
 
0.1%
0.6306057076 1
 
< 0.1%
0.7119083556 1
 
< 0.1%
0.7440802191 1
 
< 0.1%
0.687406994 1
 
< 0.1%
0.6853117643 1
 
< 0.1%
0.6604774052 1
 
< 0.1%
0.7204512742 1
 
< 0.1%
Other values (2790) 2790
99.5%
ValueCountFrequency (%)
0.4545377868 1
< 0.1%
0.4665426918 1
< 0.1%
0.4750500848 1
< 0.1%
0.4811106853 1
< 0.1%
0.5125109553 1
< 0.1%
0.5189921069 1
< 0.1%
0.5216779019 1
< 0.1%
0.5260974521 1
< 0.1%
0.5262999181 1
< 0.1%
0.5306523297 1
< 0.1%
ValueCountFrequency (%)
0.845812508 1
< 0.1%
0.8388227104 1
< 0.1%
0.8340985182 1
< 0.1%
0.8218047988 1
< 0.1%
0.8193163857 1
< 0.1%
0.8150636943 1
< 0.1%
0.812721519 1
< 0.1%
0.8121424577 1
< 0.1%
0.8091220238 1
< 0.1%
0.8080778302 1
< 0.1%

Convex_Area
Real number (ℝ)

HIGH CORRELATION 

Distinct2737
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27696.218
Minimum6355
Maximum90642.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.0 KiB
2024-10-14T21:00:28.010198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum6355
5-th percentile11025.95
Q117088.5
median24589
Q334863.25
95-th percentile56392.8
Maximum90642.5
Range84287.5
Interquartile range (IQR)17774.75

Descriptive statistics

Standard deviation14237.348
Coefficient of variation (CV)0.51405385
Kurtosis1.6065718
Mean27696.218
Median Absolute Deviation (MAD)8388
Skewness1.2300579
Sum77632500
Variance2.0270207 × 108
MonotonicityNot monotonic
2024-10-14T21:00:28.226338image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31150 3
 
0.1%
25311.5 2
 
0.1%
33251.5 2
 
0.1%
42292 2
 
0.1%
27847 2
 
0.1%
17638 2
 
0.1%
14736 2
 
0.1%
30942.5 2
 
0.1%
29574.5 2
 
0.1%
34335 2
 
0.1%
Other values (2727) 2782
99.3%
ValueCountFrequency (%)
6355 1
< 0.1%
6522 1
< 0.1%
6532 1
< 0.1%
6558 1
< 0.1%
6960 1
< 0.1%
7254 1
< 0.1%
7429 1
< 0.1%
7455.5 1
< 0.1%
7466.5 1
< 0.1%
7473 1
< 0.1%
ValueCountFrequency (%)
90642.5 1
< 0.1%
88169 1
< 0.1%
87204 1
< 0.1%
87194.5 1
< 0.1%
85139 1
< 0.1%
85004 1
< 0.1%
83363.5 1
< 0.1%
82324.5 1
< 0.1%
81859 1
< 0.1%
81600.5 1
< 0.1%

Type
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.0 KiB
SANORA
943 
MAMRA
933 
REGULAR
927 

Length

Max length7
Median length6
Mean length5.9978594
Min length5

Characters and Unicode

Total characters16812
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMAMRA
2nd rowMAMRA
3rd rowMAMRA
4th rowMAMRA
5th rowMAMRA

Common Values

ValueCountFrequency (%)
SANORA 943
33.6%
MAMRA 933
33.3%
REGULAR 927
33.1%

Length

2024-10-14T21:00:28.399226image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-14T21:00:28.539689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
sanora 943
33.6%
mamra 933
33.3%
regular 927
33.1%

Most occurring characters

ValueCountFrequency (%)
A 4679
27.8%
R 3730
22.2%
M 1866
 
11.1%
S 943
 
5.6%
N 943
 
5.6%
O 943
 
5.6%
E 927
 
5.5%
G 927
 
5.5%
U 927
 
5.5%
L 927
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16812
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 4679
27.8%
R 3730
22.2%
M 1866
 
11.1%
S 943
 
5.6%
N 943
 
5.6%
O 943
 
5.6%
E 927
 
5.5%
G 927
 
5.5%
U 927
 
5.5%
L 927
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16812
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 4679
27.8%
R 3730
22.2%
M 1866
 
11.1%
S 943
 
5.6%
N 943
 
5.6%
O 943
 
5.6%
E 927
 
5.5%
G 927
 
5.5%
U 927
 
5.5%
L 927
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16812
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 4679
27.8%
R 3730
22.2%
M 1866
 
11.1%
S 943
 
5.6%
N 943
 
5.6%
O 943
 
5.6%
E 927
 
5.5%
G 927
 
5.5%
U 927
 
5.5%
L 927
 
5.5%

Interactions

2024-10-14T21:00:20.965319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:01.015650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:03.269039image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:04.970180image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:06.651858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:08.377531image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:11.334992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:12.963716image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:14.547754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:16.109530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:17.839167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:19.502111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:21.133400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:01.186008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:03.424152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:05.149246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:06.783931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:08.552054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:11.473051image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:13.101182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:14.666582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:16.328435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:17.957667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:19.628685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:21.284396image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:01.413392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:03.583481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:05.297300image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:06.931349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:08.738221image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:11.607280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:13.290948image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:14.784212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:16.497854image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:18.076621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:19.752310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:21.437716image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:01.585946image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:03.715499image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:05.427438image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:07.052138image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:09.010439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:11.733858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:13.441282image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:14.902629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:16.644104image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:18.330050image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:19.873864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:21.546519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:01.750769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:03.844362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:05.545794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:07.165117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:09.172125image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:11.872031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:13.573759image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:15.011923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:16.790623image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:18.469811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:19.979645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:21.719762image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:01.961077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:04.010525image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:05.679431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:07.305658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:09.355307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:12.007370image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:13.706286image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:15.125769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:16.957144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:18.648911image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:20.126542image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:21.845354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:02.111787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:04.138365image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:05.825289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:07.422295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:09.507987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:12.124338image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:13.827365image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:15.239879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:17.089941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:18.785277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:20.246552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:22.012254image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:02.433992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:04.265172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:05.956075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:07.550907image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:09.666332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:12.250764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:13.952901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:15.362473image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:17.218683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:18.920002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:20.369328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:22.172254image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:02.575272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:04.391245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:06.086723image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:07.670807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:09.809576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:12.396738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:14.076118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:15.475541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:17.342960image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:19.042472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:20.476859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:22.334372image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:02.732150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:04.559265image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:06.213487image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:07.815768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:10.840490image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:12.519634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:14.199071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:15.633148image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:17.469560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:19.165375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:20.596989image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:22.460515image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:02.931089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:04.701045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:06.348584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:07.954276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:10.979472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:12.676300image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:14.315246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:15.808540image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:17.588949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:19.271255image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:20.716352image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:22.598739image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:03.128522image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:04.849625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:06.506154image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:08.195008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:11.173563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:12.839157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:14.443186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:15.955228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:17.726876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:19.404011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T21:00:20.838838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-14T21:00:28.656102image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AreaAspect_RatioCompactnessConvex_AreaEccentricityExtentLengthPerimeterRoundnessSolidityThicknessTypeWidth
Area1.0000.0240.0690.9950.0200.3570.4560.8480.2280.1770.3440.1790.283
Aspect_Ratio0.0241.0000.1160.0290.358-0.0420.1470.066-0.202-0.0810.0130.1810.024
Compactness0.0690.1161.0000.1330.107-0.5230.1780.520-0.436-0.832-0.0280.2040.122
Convex_Area0.9950.0290.1331.0000.0240.3040.4730.8880.2010.1070.3430.1710.290
Eccentricity0.0200.3580.1070.0241.000-0.0330.1230.063-0.204-0.0580.0250.1640.012
Extent0.357-0.042-0.5230.304-0.0331.0000.0310.0200.2860.6770.0570.256-0.036
Length0.4560.1470.1780.4730.1230.0311.0000.502-0.200-0.1020.1780.2480.309
Perimeter0.8480.0660.5200.8880.0630.0200.5021.000-0.017-0.2560.2720.1920.303
Roundness0.228-0.202-0.4360.201-0.2040.286-0.200-0.0171.0000.3220.0700.321-0.023
Solidity0.177-0.081-0.8320.107-0.0580.677-0.102-0.2560.3221.0000.0500.235-0.041
Thickness0.3440.013-0.0280.3430.0250.0570.1780.2720.0700.0501.0000.1730.170
Type0.1790.1810.2040.1710.1640.2560.2480.1920.3210.2350.1731.0000.177
Width0.2830.0240.1220.2900.012-0.0360.3090.303-0.023-0.0410.1700.1771.000

Missing values

2024-10-14T21:00:22.768212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-14T21:00:23.006184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

LengthWidthThicknessAreaPerimeterRoundnessSolidityCompactnessAspect_RatioEccentricityExtentConvex_AreaType
0272.553253227.940628127.75913222619.0643.8132690.4604670.9733841.4582651.5657950.7981470.68119323237.5MAMRA
1340.942719234.188126128.19950923038.0680.9848410.4519830.9573041.6018441.5529920.7552330.65635324065.5MAMRA
2344.597992229.418610125.79654722386.5646.9432120.1783040.9672701.4877721.6962360.8451510.68362023144.0MAMRA
3367.850677232.763153125.91880822578.5661.2274830.5479650.9655121.5409791.9425380.8061220.68536023385.0MAMRA
4276.140106230.150742107.25344819068.0624.8427060.4302720.9514501.6293952.1425030.8446230.71480020041.0MAMRA
5315.898743231.914429107.75978919335.0615.3868630.6056190.9573441.5586281.7403880.8142270.72792020196.5MAMRA
6195.676468226.371048106.47940818583.5613.9726490.3866790.9562861.6142122.1344060.8083270.72790819433.0MAMRA
7227.627579226.186142102.62307718069.5584.8599520.2576670.9689781.5064261.6884120.8767960.74067518648.0MAMRA
8413.477173133.728958138.19053641492.01078.9848430.3090090.9509862.2328341.7311570.7988280.76764543630.5MAMRA
9418.210327156.352112129.65989740630.51039.4284930.2957830.9558882.1160561.6471750.8221750.75859842505.5MAMRA
LengthWidthThicknessAreaPerimeterRoundnessSolidityCompactnessAspect_RatioEccentricityExtentConvex_AreaType
2793294.598572133.201599110.65536525529.0765.3868630.3745270.9562321.8260741.8094120.7903350.68171926697.5SANORA
2794282.113983213.362000126.77848121200.5687.0853470.5478540.9451431.7720071.5081240.7677230.70857322431.0SANORA
2795420.821259206.783310127.36373120866.0674.0731560.4874000.9544411.7328661.6908990.8413580.72180721862.0SANORA
2796292.296082207.031631125.49166920319.5706.9432120.3119530.9455111.9572492.0703410.8571960.71932521490.5SANORA
2797345.911133206.845505125.98026320483.5655.8305140.5919480.9584941.6709721.6560220.7960270.73197221370.5SANORA
2798244.866592192.709366122.35650618471.5653.3452330.4569140.9310001.8389651.8128430.7621050.72573919840.5SANORA
2799366.171509186.254745118.70896117213.5581.6883790.6424950.9527061.5642341.7058850.8101620.71401618068.0SANORA
2800408.806732186.196182119.14722417510.5608.3157950.5581890.9488211.6817051.6680840.8221380.71899918455.0SANORA
2801280.646667188.660828120.63443817941.0630.7594460.3864650.9448101.7647011.7059240.7972620.73819118989.0SANORA
2802269.356903176.02363688.95507836683.5887.3107430.6437610.9473801.7079331.5302310.7569300.72242938721.0SANORA